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GSoC 2022 project

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vfdev edited this page Apr 14, 2022 · 9 revisions

Google Summer of Code 2022 project

Getting started

There few basic requirements for new contributors. They have to accomplish the following steps:

To be considered as a GSoC student, it is necessary to have at least 3 accepted PRs to the project. They can be something small like a doc fix or a simple bug fix. Please, see help wanted issues.

Projects

Improve Metrics module (175 hours)

Metrics module in PyTorch-Ignite proposes very unique features to evaluate a trained PyTorch model in an online manner and for any type of computation settings (single process or distributed configuration). Namely, the module provides essential metrics for classification/segmentation tasks and various regression metrics.

The plan for the improvements contains the following topics:

Note: task complexity is indicated as easy, medium, hard.

Coding tasks:

  • (medium) All metrics should work in distributed configuration (#1284)

    • Remains: Improve EpochMetric to correctly gather the output (see #1758) and all related metrics: PrecisionRecallCurve, AveragePrecision.
    • Explore asymmetric distributed metrics computation: PyTorch uneven distributed input support

  • (medium) Provide configurable distributed metrics reduce/gather methods (#1288 #1242)

  • (hard) Provide new metrics for object detection task: implement mean Average Precision metric

    • Implement the metric and its tests

  • (easy) Add minor improvements:

    • better support of sklearn metrics
    • classification metrics with micro/macro options

  • (medium) Work on stateful handlers (#966)

    • Implement a base class for stateful handlers
    • Improve RunningAverage metric to be able to restore internal values from a checkpoint
    • Improve existing handlers that require a state

Redesign tasks:

  • (medium) Work on enabling label-wise metrics (Accuracy etc.) for multi-label problems (#513)

    • Prototype new API to add label-wise option for multi-label metrics
    • Implement chosen API and implement the tests

  • (medium) Redesign metric classes to fix issues:

    • TODO: explain what to do. RunningAverage and Frequency metrics #1405

  • (medium) Output type (e.g. logits or probas) for mertrics input

    • Related issue and PR #1354

Expected outcome

We would expect at least 5 medium items from the above list to be done for this project. In addition, it would be nice to have a short blog post communicating about the work done.

Required skills

  • Intermediate level for Python and Git
  • Intermediate knowledge of PyTorch
  • Beginner knowledge of PyTorch-Ignite codebase

Preferred skills and mindset

  • Fluent with Python and Git
  • Already trained neural networks with PyTorch
  • Willing to maintain AI-related open-source project
  • Curiosity and motivation to learn new technical things

Complexity rating

4 / 5 or medium

Expected project size

175 hours

Potential mentors:

Examples and integrations (175 hours)

According to our Roadmap, we would like to provide more integrations with other tools to simplify Machine/Deep Learning end-to-end applications.

In this project we would like to provide more examples of PyTorch-Ignite using other tools bringing new features or insights, e.g. federated learning, interpretability, hyper-parameter tuning, etc.

  • (medium) Code an example for federated learning with PySift or any other equivalent tool (openfl, ...). #1378

  • (medium) Code an example of hyper-parameters tuning using Ray tune, #1735

  • (medium) Code an example using ffcv : https://ffcv.io/

  • (medium) Code an example using zenml or a similar tool

  • (hard) Port Detectron2 training script and use ignite instead of custom trainer.

  • (hard) Define a scope and code an example using pytorch Distributed RPC framework.

We can discuss and add other integrations during the GSoC program if needed.

We may decide later about example format: a tutorial or plain script or a template to our code-generator. Writing tutorials may be more time expensive but better for visibilty. Minimal requirement is a python script. Providing a code-generator template is desirable.

Expected outcome

We would expect at least 4 medium items (hard item == 2 medium) from the above list to be done for this project. In addition, it would be nice to have a short blog post communicating about the work done.

Required skills

  • Intermediate level for Python and Git
  • Intermediate knowledge of PyTorch
  • Beginner knowledge of PyTorch-Ignite codebase

Preferred skills and mindset

  • Fluent with Python
  • Already trained neural networks with PyTorch
  • Willing to maintain AI-related open-source project
  • Curiosity and motivation to learn new technical things

Complexity rating

4 / 5 or medium

Expected project size

175 hours

Potential mentors:

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